Driving safety assessment method for high-speed maglev-bridge systems based on probabilistic tail distribution
-
摘要:
为解决传统安全评估方法因忽视列车运营量累积效应而导致高速磁浮-桥梁系统行车安全评估不准确的问题，构建了一种基于行车安全指标尾部分布的概率性安全评估方法。通过方幂好格子点法、多体动力学和模态叠加法建立了高速磁浮-轨道梁耦合随机动力学模型；采用等概率法和极值理论求得行车安全指标的概率特征参数（均值、标准差），构建了概率特征参数与极值分布函数之间的显式映射关系；依据预设置信度求得安全指标的极值估计量，在与现行规范阈值比较的基础上，构建了一种基于概率尾部分布特性的行车安全评判方法；以高速磁浮列车通过24.768 m轨道梁为例，开展了不同车速等工况条件下行车安全评估。结果表明：采用方幂好格子点法构建高均匀性轨道不平顺样本集，仅需131个样本即可满足计算需求，相较于蒙特卡洛法所需的5 000个样本，显著提升了行车安全指标概率特征参数的计算效率与精度；提出的极值分布能有效表征行车安全指标的概率尾部分布特性，其分布曲线能较好拟合最大值样本，估算极值亦可充分包络蒙特卡洛法生成的结果。基于尾部分布模型评估的失效样本数不足10个，而基于正态分布假设的失效样本高达2 280个，有效避免了传统方法不能考虑列车运营累积效应的局限性，为高速磁浮-桥梁系统安全评估提供科学依据。
Abstract:In response to inaccurate safety assessments in high-speed maglev-bridge systems due to the neglected cumulative train operation effect of the traditional methods, a probabilistic safety assessment method based on the tail distribution of driving safety indicators was developed. A high-speed maglev-track bridge coupled stochastic dynamic model was established using the square power good lattice point method, multibody dynamics, and the modal superposition method. The probabilistic characteristic parameters of driving safety indicators (mean and standard deviation) were determined using the equal probability method and extreme-value theory. An explicit mapping relationship between the probabilistic characteristic parameters and the extreme-value cumulative distribution function (CDF) was constructed. According to a predefined confidence level, the extreme-value estimate of the safety indicator was obtained. By comparing this with the current normative threshold, a driving safety assessment method based on the probabilistic tail distribution characteristics was proposed. Finally, taking the case of a high-speed maglev train passing over a 24.768 m track beam, a driving safety assessment was conducted under different operating conditions such as varying train speeds. The results indicate that by employing the square power good lattice point method to construct a highly uniform set of track irregularity samples, the computational requirements can be met with only 131 samples. Compared to the 5 000 samples required by the Monte Carlo method, this significantly improves both the computational efficiency and accuracy of the probabilistic characteristic parameters for driving safety indicators. Furthermore, the extreme-value distribution proposed can effectively characterize the tail distribution characteristics of driving safety indicators. Its distribution curve well fits the maximum value samples, and the estimated extreme values can fully envelope the results generated by the Monte Carlo method. The number of failed samples evaluated based on the tail distribution model is less than 10, whereas it is as high as 2 280 under the normal distribution assumption. The method effectively avoids the limitation of traditional methods in failing to account for the cumulative effect of train operations, thereby providing a scientific basis for the safety assessment of high-speed maglev-bridge systems.
-
图 7 v=430 km·h-1下的极值概率曲线拟合
Figure 7. Extreme value probability curve fitting at v=430 km·h-1
图 8 v=600 km·h-1下的极值概率曲线拟合
Figure 8. Extreme value probability curve fitting at v=600 km·h-1
图 9 v=430 km·h-1下的极值样本检验
Figure 9. Inspection of extreme value samples at v=430 km·h-1
图 10 v=600 km·h-1下的极值样本检验
Figure 10. Inspection of extreme value samples at v=600 km·h-1
表 1 计算时间
Table 1. Computation time
车速/（km·h-1） 计算时间/h 本文方法 蒙特卡洛法 430 4.9 188.0 600 5.7 218.5 
下载: 导出CSV
表 2 失效样本数
Table 2. Numbers of failed sample
分布形式 430 km·h-1工况的失效样本数 600 km·h-1工况的失效样本数 悬浮磁铁 导向磁铁 悬浮磁铁 导向磁铁 正态分布 2 230 2 365 2 257 2 228 尾部分布 8 3 10 4
下载: 导出CSV
-
[1] 李苗, 马卫华, 龚俊虎, 等. 中低速磁浮车辆-桥梁耦合系统动力性能试验[J]. 交通运输工程学报, 2022, 22(1): 141-154. doi: 10.19818/j.cnki.1671-1637.2022.01.012 LI Miao, MA Wei-hua, GONG Jun-hu, et al. Dynamic performance test of medium and low speed maglev vehicle-bridge coupled system[J]. Journal of Traffic and Transportation Engineering, 2022, 22(1): 141-154. doi: 10.19818/j.cnki.1671-1637.2022.01.012 [2] 过振宇, 李中奇, 谭畅. 基于自适应PSO的DMC-FOPID永磁磁浮列车速度跟踪控制[J]. 铁道学报, 2023, 45(9): 74-84.GUO Zhen-yu, LI Zhong-qi, TAN Chang. Speed tracking control of permanent magnet maglev trains based on DMC-FOPID control algorithm and adaptive PSO[J]. Journal of the China Railway Society, 2023, 45(9): 74-84. [3] 孙友刚, 黄志创, 林国斌, 等. 高速磁浮车辆悬浮系统多元基神经网络容错控制[J]. 交通运输工程学报, 2025, 25(2): 61-74. doi: 10.19818/j.cnki.1671-1637.2025.02.004 SUN You-gang, HUANG Zhi-chuang, LIN Guo-bin, et al. Fault-tolerant control for levitation systems of high-speed maglev train based on diversified basis neural networks[J]. Journal of Traffic and Transportation Engineering, 2025, 25(2): 61-74. doi: 10.19818/j.cnki.1671-1637.2025.02.004 [4] 余浩伟, 寇峻瑜, 李艳. 600 km·h-1高速磁浮在国内的适应性及工程化发展[J]. 铁道工程学报, 2020, 37(12): 16-20, 88.YU Hao-wei, KOU Jun-yu, LI Yan. Adaptability and engineering development of 600 km·h-1 high-speed maglev in China[J]. Journal of Railway Engineering Society, 2020, 37(12): 16-20, 88. [5] WANG Z L, XU Y L, LI G Q, et al. Modelling and validation of coupled high-speed maglev train-and-viaduct systems considering support flexibility[J]. Vehicle System Dynamics, 2019, 57(2): 161-191. doi: 10.1080/00423114.2018.1450517 [6] 罗英昆, 赵春发, 梁鑫, 等. 小半径竖曲线上磁浮车辆空气弹簧动态响应分析[J]. 振动与冲击, 2020, 39(17): 99-105.LUO Ying-kun, ZHAO Chun-fa, LIANG Xin, et al. Dynamic responses of air-spring suspension of a maglev vehicle negotiating a small-radius vertical curved track[J]. Journal of Vibration and Shock, 2020, 39(17): 99-105. [7] 梁鑫, 马卫华. 2种磁轨关系的磁浮车桥相互作用比较分析[J]. 铁道科学与工程学报, 2017, 14(4): 845-851.LIANG Xin, MA Wei-hua. Comparative analysis of two kinds of magnet-track relationship of maglev vehicle and guideway interaction[J]. Journal of Railway Science and Engineering, 2017, 14(4): 845-851. [8] 李小珍, 王渝文, 胡启凯, 等. 不同桥跨对中低速磁浮列车-简支梁系统竖向耦合振动影响机理研究[J]. 振动与冲击, 2022, 41(19): 8-15.LI Xiao-zhen, WANG Yu-wen, HU Qi-kai, et al. Influence mechanisms of different bridge spans on vertical coupled vibration of low and medium speed maglev train-simply supported beam system[J]. Journal of Vibration and Shock, 2022, 41(19): 8-15. [9] LIU W, GUO W H. Vibration analysis of EMS-type maglev vehicles traveling over a long-span bridge with double lines[J]. KSCE Journal of Civil Engineering, 2020, 24(5): 1531-1544. doi: 10.1007/s12205-020-0816-5 [10] 晋智斌, 强士中, 李小珍. 轨道不平顺激励下车辆-桥梁垂向随机振动方差解法[J]. 铁道学报, 2008, 30(6): 63-68.JIN Zhi-bin, QIANG Shi-zhong, LI Xiao-zhen. Covariance method for vehicle-bridge vertical stochastic vibration excited by rail irregularities[J]. Journal of the China Railway Society, 2008, 30(6): 63-68. [11] XIAO X, YAN Y, CHEN B. Stochastic dynamic analysis for vehicle-track-bridge system based on probability density evolution method[J]. Engineering Structures, 2019, 188: 745-761. doi: 10.1016/j.engstruct.2019.02.042 [12] 苏成, 徐瑞. 非平稳随机激励下结构体系动力可靠度时域解法[J]. 力学学报, 2010, 42(3): 512-520.SU Cheng, XU Rui. Time-domain method for dynamic reliability of structural systems subjected to non-stationary random excitations[J]. Chinese Journal of Theoretical and Applied Mechanics, 2010, 42(3): 512-520. [13] ZHANG Z C, LIN J H, ZHANG Y H, et al. Non-stationary random vibration analysis for train-bridge systems subjected to horizontal earthquakes[J]. Engineering Structures, 2010, 32(11): 3571-3582. doi: 10.1016/j.engstruct.2010.08.001 [14] XIN L F, LI X Z, ZHU Y, et al. Uncertainty and sensitivity analysis for train-ballasted track-bridge system[J]. Vehicle System Dynamics, 2020, 58(3): 453-471. doi: 10.1080/00423114.2019.1584678 [15] SU C, XU R. Random vibration analysis of structures by a time-domain explicit formulation method[J]. Structural Engineering and Mechanics, 2014, 52(2): 239-260. doi: 10.12989/sem.2014.52.2.239 [16] 刘伟, 赵春发, 娄会彬, 等. 基于虚拟激励法的磁浮车桥耦合系统随机振动分析[J]. 西南交通大学学报, 2024, 59(4): 823-831.LIU Wei, ZHAO Chun-fa, LOU Hui-bin, et al. Stochastic vibration analysis of maglev train-bridge coupling system based on pseudo excitation method[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 823-831. [17] 余志武, 张鹏, 丁叁叁, 等. 基于概率密度演化理论的磁浮列车-轨道梁耦合系统随机动力分析[J]. 中南大学学报(自然科学版), 2022, 53(7): 2544-2554.YU Zhi-wu, ZHANG Peng, DING San-san, et al. Stochastic vibration analysis of maglev vehicle-guideway coupling system based on PDEM[J]. Journal of Central South University (Science and Technology), 2022, 53(7): 2544-2554. [18] 陆周瑞, 陈冉, 苏成. 磁浮车辆-桥梁耦合系统随机振动分析的时域显式方法研究[J]. 工程力学, 2022, 39(8): 19-30.LU Zhou-rui, CHEN Ran, SU Cheng. Random vibration analysis of maglev vehicle-bridge coupled systems with the explicit time-domain method[J]. Engineering Mechanics, 2022, 39(8): 19-30. [19] 周子骥, 张楠. 采用同分布概率演化法求解脱轨系数的概率分布[J]. 铁道学报, 2023, 45(12): 131-137.ZHOU Zi-ji, ZHANG Nan. Probability distribution of derailment factor using identical probability distribution evolution method[J]. Journal of the China Railway Society, 2023, 45(12): 131-137. [20] XIANG H Y, TIAN X F, LI Y L, et al. Dynamic interaction analysis of high-speed maglev train and guideway with a control loop failure[J]. International Journal of Structural Stability and Dynamics, 2022, 22(10): 1-28. [21] 汤港归, 汪斌, 夏翠鹏, 等. 磁浮车辆起浮参数控制及其动力性能研究[J]. 铁道科学与工程学报, 2023, 20(3): 790-801.TANG Gang-gui, WANG Bin, XIA Cui-peng, et al. Control of levitation parameters and dynamic performance of maglev vehicle[J]. Journal of Railway Science and Engineering, 2023, 20(3): 790-801. [22] SHI J, FANG W S, WANG Y J, et al. Measurements and analysis of track irregularities on high-speed maglev lines[J]. Journal of Zhejiang University Science A, 2014, 15(6): 385-394. doi: 10.1631/jzus.A1300163 [23] ZHOU Z J, LIU W, TAO Q, et al. Study on driving safety of vehicle-bridge interaction system based on ultra-high-dimensional point-selected extremum methods[J]. International Journal of Rail Transportation, 2025, 13(3): 614-633. doi: 10.1080/23248378.2024.2354679 [24] ZHANG N, XIA H. Dynamic analysis of coupled vehicle–bridge system based on inter-system iteration method[J]. Computers & Structures, 2013, 114/115: 26-34. [25] 徐磊, 翟婉明. 轨道不平顺概率模型[J]. 交通运输工程学报, 2018, 18(3): 56-63. doi: 10.19818/j.cnki.1671-1637.2018.03.006 XU Lei, ZHAI Wan-ming. Track irregularity probabilistic model[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 56-63. doi: 10.19818/j.cnki.1671-1637.2018.03.006 [26] 周子骥, 张楠, 孙琪凯. 基于虚拟激励法列车脱轨系数概率统计研究[J]. 工程力学, 2022, 39(1): 219-227.ZHOU Zi-ji, ZHANG Nan, SUN Qi-kai. The probability statistics of train derailment coefficients based on the pseudo-excitation method[J]. Engineering Mechanics, 2022, 39(1): 219-227. -
计量
- 文章访问数: 18
- HTML全文浏览量: 10
- PDF下载量: 4
- 被引次数: 0
